Master 2019/2020

Applied Artificial Intelligence with Deep Learning

Type: Elective course (Big Data Systems)

Area of studies: Business Informatics

Delivered by: Department of Innovation and Business in Information Technologies

Where: Graduate School of Business

When: 2 year, 1 module

Mode of studies: distance learning

Instructors: Dmitry Shostko

Master’s programme: Big Data Systems

Language: English

ECTS credits: 6

Contact hours: 2

Course Syllabus

Abstract

First we’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life.

Learning Objectives

• This course gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

Expected Learning Outcomes

• Can identify a problem suitable for machine learning
• Know the fundamentals of Linear Algebra and Neural Networks
• Knows and can use the most popular DeepLearning Frameworks
• Knows at least a few modern applications of machine learning
• Know how to build up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases
• Know how to scale the artificial brains using Kubernetes, Apache Spark and GPUs.

Course Contents

• Introduction to Deep Learning
  We’ll learn about the fundamentals of Linear Algebra and Neural Networks.
• Deep Learning Frameworks
  We introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course.
• DeepLearning Applications
  We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases.
• Scaling and Deployment
  We learn how to scale the artificial brains using Kubernetes, Apache Spark and GPUs. Scaling Neural Networks with Apache SystemML Scaling a Neural Network with DeepLearning4J Scaling Neural Networks with IBM Watson

Assessment Elements

• Visiting
• Current knowledge control
• Final examination

Interim Assessment

• Interim assessment (1 module)
  0.3 * Current knowledge control + 0.4 * Final examination + 0.3 * Visiting

Bibliography

Recommended Core Bibliography

• Taweh Beysolow II. (2017). Introduction to Deep Learning Using R. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.1.4842.2734.3

Recommended Additional Bibliography

• Huang, K., Hussain, A., Wang, Q.-F., & Zhang, R. (2019). Deep Learning: Fundamentals, Theory and Applications. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2029631
• Kelleher, J. D. (2019). Deep Learning. Cambridge: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2234376
• Kienzler, R. (2017). Mastering Apache Spark 2.x - Second Edition (Vol. 2nd ed). Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1562681